Impact of generative AI dialogic feedback on different stages of programming problem solving
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AI adjusts the specificity, depth, and difficulty of feedback based on students' abilities and prior performance, enabling tailored prompts and tiered guidance.
“AI adjusts the specificity, depth, and difficulty of feedback based on students' abilities and prior performance, enabling tailored prompts and tiered guidance.”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [150]
A second stream configures AI as a just-in-time tutor, providing stepwise explanations or sample code when students encounter obstacles, with teachers monitoring and intervening only when needed.
“A second stream configures AI as a just-in-time tutor. When students encounter obstacles in programming or laboratory work, they query the model and receive stepwise explanations or sample code; teachers monitor frequency and content via dashboards and intervene only when AI misinterprets a problem or students stall for too long [101,148,150].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [150]
Evidence spans STEM and the humanities, including gains in understanding physics and programming, as well as improved reading and poetry interpretation.
“Evidence spans STEM and the humanities, including gains in understanding physics [91,101,104,147] and programming [95,148,150], as well as improved reading and poetry interpretation [72,75,81,116].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [150]
Over-reliance on AI for brainstorming or direct reuse can dampen originality and depth of creation.
“The deeper consequence is a drag on core capability development: over-reliance on AI for brainstorming or direct reuse can dampen originality and depth of creation [117,135,150]”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [150]
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414 · see citing works
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 1× · see citing works
“ChatGPT provided code fragments and, occasion - ally, entire codes (Kasneci et al., 2023 ; Wu et al., 2024 ; Chiu, 2024 ).”
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · cited 1× · see citing works
“Following the approach proposed by Liu et al. ( 2023 ), prompt engineering was employed to make the responses of the GenAIDF more targeted and coherent.”
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · cited 1× · see citing works
“Following the approach proposed by Liu et al. ( 2023 ), prompt engineering was employed to make the responses of the GenAIDF more targeted and coherent.”
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004 · see citing works
Students' voices on generative ai: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · arXiv · doi:10.48550/arxiv.2305.00290 · Cited by 2,504 · cited 1× · see citing works
“According to Chan and Hu ( 2023 ) and Cooper ( 2023 ), GenAIDF may foster a dependency that hinders thorough thinking and independent problem-solving.”
The development of student feedback literacy: Enabling uptake of feedback
Carless, D., & Boud, D. (2018) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2018.1463354 · Cited by 2,428 · see citing works
Students’ voices on generative AI: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00411-8 · Cited by 2,301 · cited 1× · see citing works
“According to Chan and Hu ( 2023 ) and Cooper ( 2023 ), GenAIDF may foster a dependency that hinders thorough thinking and independent problem-solving.”
ChatGPT for Language Teaching and Learning
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023) · RELC Journal · doi:10.1177/00336882231162868 · Cited by 1,371 · cited 1× · see citing works
“As artificial intelligence advances, GenAIDF has been gradually replacing traditional dialogue formats (Kohnke et al., 2023 ; Mahapatra, 2024 ).”
Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence
Cooper, G. (2023) · Journal of Science Education and Technology · doi:10.1007/s10956-023-10039-y · Cited by 1,263 · cited 2× · see citing works
“However, in the code-writing stage, the humanoid language expression of ChatGPT may lead students to trust its feedback information unquestioningly, thereby overlooking the truthfulness and accuracy of the information (Cooper, 2023 ).”
ChatGPT for Education and Research: Opportunities, Threats, and Strategies
Rahman, M. M., & Watanobe, Y. (2023) · Applied Sciences · doi:10.3390/app13095783 · Cited by 1,080 · cited 3× · see citing works
“In addition, GenAIDF can create explanations and step-by-step solutions for given problems, which develops students’ problem-solving skills and creative thinking (Rahman & Watanobe, 2023 ).”
InfluentialImpact of ChatGPT on ESL students’ academic writing skills: a mixed methods intervention study
Mahapatra, S. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00295-9 · Cited by 398 · see citing works
Use chat gpt to solve programming bugs
Surameery, N. M. S., & Shakor, M. Y. (2023) · International Journal of Information technology and Computer Engineering · doi:10.55529/ijitc.31.17.22 · Cited by 382 · see citing works
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100005 · Cited by 344 · see citing works
Lee, H.-Y., Chen, P.-H., Wang, W.-S., Huang, Y.-M., & Wu, T.-T. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00447-4 · Cited by 273 · cited 1× · see citing works
“Consequently, drawing on the perspectives of Balreira et al. ( 2023) and Lee et al. ( 2024 ), we proposed that students initially generate pseudocode from problem descriptions during the algorithmic modeling stage to cultivate independent thinking and problem-solving abilities.”
Chaudhry, I. S., Sarwary, S. A. M., El Refae, G. A., & Chabchoub, H. (2023) · Cogent Education · doi:10.1080/2331186x.2023.2210461 · Cited by 262 · see citing works
Chiu, T. K. F. (2024) · Educational technology research and development · doi:10.1007/s11423-024-10366-w · Cited by 235 · see citing works
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Liu, X., & Zhong, B. (2025) · Educational Research Review · doi:10.1016/j.edurev.2025.100741 · Cited by 78 · see citing works
Zhao, G., Yang, L., Hu, B., & Wang, J. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251336154 · Cited by 27 · see citing works
Lin, X., & Tan, H. (2025) · Systems · doi:10.3390/systems13100840 · Cited by 22 · see citing works
Zhang, L., Jiang, Q., Xiong, W., & Zhao, W. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251333874 · Cited by 15 · see citing works
- Authors
- Gong, X., Li, Z., & Qiao, A.
- Affiliations
- Capital Normal University (CN)
- Corresponding author
- Qiao, Ailing
- Venue
- Education and Information Technologies
- Published
- 2024-12-09
- Volume
- 30
- Issue
- 7
- Pages
- 9689-9709
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W4405166382
- Cited by
- 26
- References
- 17
- Citations by year
- 2026: 13 · 2025: 13
- FWCI
- 9.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Education and Information Technologies (publishedVersion)
- Funders
- the National Education Scientific Planning Projects of China (DCA220449)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- programming problem solving, high school students, programming skills, critical thinking, writing software, debugging, plagiarism, student engagement
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